Increasing the Preparedness through Participatory Action Research in the Implementation of the Disaster Resilient Village Program in Madegondo Village
Bibliographic record
Abstract
Climate change is increasing the frequency of floods in Indonesia, thereby triggering the need for effective disaster management down to detailed levels such as the Destana Program. Madegondo Village, Sukoharjo Regency experiences floods every year. It has become the focus of research to improve community preparedness using the Participatory Action Research (PAR) method with Focus Group Discussion (FGD) as the main technique. This research revealed that Madegondo Village is vulnerable to tornadoes, fires, and dengue fever. Risk analysis indicates a moderate level of danger in affecting human, economic, infrastructure, environmental, and socio-political assets. Furthermore, the creation of flood disaster risk maps, the Disaster Risk Reduction Forum, and disaster management plans were also carried out based on community participation. An early warning system was also developed via telephone and WhatsApp based on data from Kaliwingko and Bengawan Solo River.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".